OpenRaiser/Intern-Atlas huggingface dataset:
Generating Diverse Indoor Furniture Arrangements
Venue: arXiv cs.LG 2022
Year: 2022

We present a method for generating arrangements of indoor furniture from human-designed furniture layout data. Our method creates arrangements that target specified diversity, such as the total price of all furniture in the room and the number of pieces placed. To generate realistic furniture arrangement, we train a generative adversarial network (GAN) on human-designed layouts. To target specific diversity in the arrangements, we optimize the latent space of the GAN via a quality diversity algorithm to generate a diverse arrangement collection. Experiments show our approach discovers a set of arrangements that are similar to human-designed layouts but varies in price and number of furniture pieces.
CoCoPIE XGen: A Full-Stack AI-Oriented Optimizing Framework
Venue: arXiv cs.LG 2022
Year: 2022

There is a growing demand for shifting the delivery of AI capability from data centers on the cloud to edge or end devices, exemplified by the fast emerging real-time AI-based apps running on smartphones, AR/VR devices, autonomous vehicles, and various IoT devices. The shift has however been seriously hampered by the large growing gap between DNN computing demands and the computing power on edge or end devices. This article presents the design of XGen, an optimizing framework for DNN designed to bridge the gap. XGen takes cross-cutting co-design as its first-order consideration. Its full-stack AI-oriented optimizations consist of a number of innovative optimizations at every layer of the DNN software stack, all designed in a cooperative manner. The unique technology makes XGen able to optimize various DNNs, including those with an extreme depth (e.g., BERT, GPT, other transformers), and generate code that runs several times faster than those from existing DNN frameworks, while delivering the same level of accuracy.
Natural Backdoor Datasets
Venue: arXiv cs.CV 2022
Year: 2022

Extensive literature on backdoor poison attacks has studied attacks and defenses for backdoors using "digital trigger patterns." In contrast, "physical backdoors" use physical objects as triggers, have only recently been identified, and are qualitatively different enough to resist all defenses targeting digital trigger backdoors. Research on physical backdoors is limited by access to large datasets containing real images of physical objects co-located with targets of classification. Building these datasets is time- and labor-intensive. This works seeks to address the challenge of accessibility for research on physical backdoor attacks. We hypothesize that there may be naturally occurring physically co-located objects already present in popular datasets such as ImageNet. Once identified, a careful relabeling of these data can transform them into training samples for physical backdoor attacks. We propose a method to scalably identify these subsets of potential triggers in existing datasets, along with the specific classes they can poison. We call these naturally occurring trigger-class subsets natural backdoor datasets. Our techniques successfully identify natural backdoors in widely-available datasets, and produce models behaviorally equivalent to those trained on manually curated datasets. We release our code to allow the research community to create their own datasets for research on physical backdoor attacks.
Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective
Venue: arXiv cs.LG 2022
Year: 2022

The problem of out-of-distribution detection for graph classification is far from being solved. The existing models tend to be overconfident about OOD examples or completely ignore the detection task. In this work, we consider this problem from the uncertainty estimation perspective and perform the comparison of several recently proposed methods. In our experiment, we find that there is no universal approach for OOD detection, and it is important to consider both graph representations and predictive categorical distribution.
A consistent and flexible framework for deep matrix factorizations
Venue: arXiv cs.LG 2022
Year: 2022

Deep matrix factorizations (deep MFs) are recent unsupervised data mining techniques inspired by constrained low-rank approximations. They aim to extract complex hierarchies of features within high-dimensional datasets. Most of the loss functions proposed in the literature to evaluate the quality of deep MF models and the underlying optimization frameworks are not consistent because different losses are used at different layers. In this paper, we introduce two meaningful loss functions for deep MF and present a generic framework to solve the corresponding optimization problems. We illustrate the effectiveness of this approach through the integration of various constraints and regularizations, such as sparsity, nonnegativity and minimum-volume. The models are successfully applied on both synthetic and real data, namely for hyperspectral unmixing and extraction of facial features.
Exploring the Effectiveness of Self-supervised Learning and Classifier Chains in Emotion Recognition of Nonverbal Vocalizations
Venue: arXiv cs.SD 2022
Year: 2022

We present an emotion recognition system for nonverbal vocalizations (NVs) submitted to the ExVo Few-Shot track of the ICML Expressive Vocalizations Competition 2022. The proposed method uses self-supervised learning (SSL) models to extract features from NVs and uses a classifier chain to model the label dependency between emotions. Experimental results demonstrate that the proposed method can significantly improve the performance of this task compared to several baseline methods. Our proposed method obtained a mean concordance correlation coefficient (CCC) of $0.725$ in the validation set and $0.739$ in the test set, while the best baseline method only obtained $0.554$ in the validation set. We publicate our code at https://github.com/Aria-K-Alethia/ExVo to help others to reproduce our experimental results.
TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learning
Venue: arXiv cs.CV 2022
Year: 2022

We present Transformation Invariance and Covariance Contrast (TiCo) for self-supervised visual representation learning. Similar to other recent self-supervised learning methods, our method is based on maximizing the agreement among embeddings of different distorted versions of the same image, which pushes the encoder to produce transformation invariant representations. To avoid the trivial solution where the encoder generates constant vectors, we regularize the covariance matrix of the embeddings from different images by penalizing low rank solutions. By jointly minimizing the transformation invariance loss and covariance contrast loss, we get an encoder that is able to produce useful representations for downstream tasks. We analyze our method and show that it can be viewed as a variant of MoCo with an implicit memory bank of unlimited size at no extra memory cost. This makes our method perform better than alternative methods when using small batch sizes. TiCo can also be seen as a modification of Barlow Twins. By connecting the contrastive and redundancy-reduction methods together, TiCo gives us new insights into how joint embedding methods work.
Mobile Mental Health Apps: Alternative Intervention or Intrusion?
Venue: arXiv cs.CY 2022
Year: 2022

Mental health is an extremely important subject, especially in these unprecedented times of the COVID-19 pandemic. Ubiquitous mobile phones can equip users to supplement psychiatric treatment and manage their mental health. Mobile Mental Health (MMH) apps emerge as an effective alternative to assist with a broad range of psychological disorders filling the much-needed patient-provider accessibility gap. However, it also raises significant concerns with sensitive information leakage.The absence of a transparent privacy policy and lack of user awareness may pose a significant threat to undermining the applicability of such tools. We conducted a multifold study of - 1) Privacy Policies (Manually and with Polisis, an automated framework to evaluate privacy policies); 2) App permissions; 3) Static Analysis for inherent security issues; 4) Dynamic Analysis for threat surface and vulnerabilities detection, and 5) Traffic Analysis. Our results indicate that apps' exploitable flaws, dangerous permissions, and insecure data handling pose a potential threat to the users' privacy and security. The Dynamic analysis identified 145 vulnerabilities in 20 top-rated MMH apps where attackers and malicious apps can access sensitive information. 45% of MMH apps use a unique identifier, Hardware Id, which can link a unique id to a particular user and probe users' mental health. Traffic analysis shows that sensitive mental health data can be leaked through insecure data transmission. MMH apps need better scrutiny and regulation for more widespread usage to meet the increasing need for mental health care without being intrusive to the already vulnerable population.
Imitate then Transcend: Multi-Agent Optimal Execution with Dual-Window Denoise PPO
Venue: arXiv cs.LG 2022
Year: 2022

A novel framework for solving the optimal execution and placement problems using reinforcement learning (RL) with imitation was proposed. The RL agents trained from the proposed framework consistently outperformed the industry benchmark time-weighted average price (TWAP) strategy in execution cost and showed great generalization across out-of-sample trading dates and tickers. The impressive performance was achieved from three aspects. First, our RL network architecture called Dual-window Denoise PPO enabled efficient learning in a noisy market environment. Second, a reward scheme with imitation learning was designed, and a comprehensive set of market features was studied. Third, our flexible action formulation allowed the RL agent to tackle optimal execution and placement collectively resulting in better performance than solving individual problems separately. The RL agent's performance was evaluated in our multi-agent realistic historical limit order book simulator in which price impact was accurately assessed. In addition, ablation studies were also performed, confirming the superiority of our framework.
Warm-Starting in Message Passing algorithms
Venue: arXiv cs.IT 2022
Year: 2022

Vector Approximate Message Passing (VAMP) provides the means of solving a linear inverse problem in a Bayes-optimal way assuming the measurement operator is sufficiently random. However, VAMP requires implementing the linear minimum mean squared error (LMMSE) estimator at every iteration, which makes the algorithm intractable for large-scale problems. In this work, we present a class of warm-started (WS) methods that provides a scalable approximation of LMMSE within VAMP. We show that a Message Passing (MP) algorithm equipped with a method from this class can converge to the fixed point of VAMP while having a per-iteration computational complexity proportional to that of AMP. Additionally, we provide the Onsager correction and a multi-dimensional State Evolution for MP utilizing one of the WS methods. Lastly, we show that the approximation approach used in the recently proposed Memory AMP (MAMP) algorithm is a special case of the developed class of WS methods.
Imitation Learning for Generalizable Self-driving Policy with Sim-to-real Transfer
Venue: arXiv cs.LG 2022
Year: 2022

Imitation Learning uses the demonstrations of an expert to uncover the optimal policy and it is suitable for real-world robotics tasks as well. In this case, however, the training of the agent is carried out in a simulation environment due to safety, economic and time constraints. Later, the agent is applied in the real-life domain using sim-to-real methods. In this paper, we apply Imitation Learning methods that solve a robotics task in a simulated environment and use transfer learning to apply these solutions in the real-world environment. Our task is set in the Duckietown environment, where the robotic agent has to follow the right lane based on the input images of a single forward-facing camera. We present three Imitation Learning and two sim-to-real methods capable of achieving this task. A detailed comparison is provided on these techniques to highlight their advantages and disadvantages.
AMDS Symbol-Pair Codes from Repeated-Root Cyclic Codes
Venue: arXiv cs.IT 2022
Year: 2022

Symbol-pair codes are proposed to guard against pair-errors in symbol-pair read channels. The minimum symbol-pair distance is of significance in determining the error-correcting capability of a symbol-pair code. One of the central themes in symbol-pair coding theory is the constructions of symbol-pair codes with largest possible minimum pair distance. Maximum distance separable (MDS) and almost maximum distance separable (AMDS) symbol-pair codes are optimal and sub-optimal regarding to the Singleton bound, respectively. In this paper, six new classes of AMDS symbol-pair codes are explicitly constructed through repeated-root cyclic codes and one class of such codes has unbounded lengths and the minimum symbol-pair distance can reach $13$.
Coupling Visual Semantics of Artificial Neural Networks and Human Brain Function via Synchronized Activations
Venue: arXiv cs.CV 2022
Year: 2022

Artificial neural networks (ANNs), originally inspired by biological neural networks (BNNs), have achieved remarkable successes in many tasks such as visual representation learning. However, whether there exists semantic correlations/connections between the visual representations in ANNs and those in BNNs remains largely unexplored due to both the lack of an effective tool to link and couple two different domains, and the lack of a general and effective framework of representing the visual semantics in BNNs such as human functional brain networks (FBNs). To answer this question, we propose a novel computational framework, Synchronized Activations (Sync-ACT), to couple the visual representation spaces and semantics between ANNs and BNNs in human brain based on naturalistic functional magnetic resonance imaging (nfMRI) data. With this approach, we are able to semantically annotate the neurons in ANNs with biologically meaningful description derived from human brain imaging for the first time. We evaluated the Sync-ACT framework on two publicly available movie-watching nfMRI datasets. The experiments demonstrate a) the significant correlation and similarity of the semantics between the visual representations in FBNs and those in a variety of convolutional neural networks (CNNs) models; b) the close relationship between CNN's visual representation similarity to BNNs and its performance in image classification tasks. Overall, our study introduces a general and effective paradigm to couple the ANNs and BNNs and provides novel insights for future studies such as brain-inspired artificial intelligence.
Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI
Venue: arXiv cs.AI 2022
Year: 2022

Recent years have seen a surge of interest in the field of explainable AI (XAI), with a plethora of algorithms proposed in the literature. However, a lack of consensus on how to evaluate XAI hinders the advancement of the field. We highlight that XAI is not a monolithic set of technologies -- researchers and practitioners have begun to leverage XAI algorithms to build XAI systems that serve different usage contexts, such as model debugging and decision-support. Algorithmic research of XAI, however, often does not account for these diverse downstream usage contexts, resulting in limited effectiveness or even unintended consequences for actual users, as well as difficulties for practitioners to make technical choices. We argue that one way to close the gap is to develop evaluation methods that account for different user requirements in these usage contexts. Towards this goal, we introduce a perspective of contextualized XAI evaluation by considering the relative importance of XAI evaluation criteria for prototypical usage contexts of XAI. To explore the context dependency of XAI evaluation criteria, we conduct two survey studies, one with XAI topical experts and another with crowd workers. Our results urge for responsible AI research with usage-informed evaluation practices, and provide a nuanced understanding of user requirements for XAI in different usage contexts.
Exploring user needs in relation to algorithmically constructed classifications of publications
Venue: arXiv cs.DL 2022
Year: 2022

Algorithmic classification of research publications has been created to study different aspects of research. Such classifications can be used to support information needs in universities for decision making. However, the classifications have foremost been evaluated quantitatively regarding their content, but not qualitatively regarding their feasibility in a specific context. The aim of this study was to explore and evaluate the usefulness of such classifications to users in the context of exploring an emerging research area. I conducted four interviews with managers of a project aimed to support research and application of artificial intelligence at the Swedish medical university Karolinska Institutet. The interviews focused on the information need of the managers. To support the project, a classification was created by clustering of publications in a citation network. A cluster map based on this classification was provided to the project leader and one interview focused on the use of the classification in the project in relation to the stated information needs. The interviews showed that the aim of the project was to improve competence, enhance communication between researchers and develop support structures. Getting an overview of artificial intelligence at the university and information about who is doing what was important to fulfill this aim. The cluster map was used to support activities conducted by the project leader, such as interviews and information gathering. It was also used to get overview and display of AI research at KI. Interpretation was found to be challenging in some cases. The interactivity of the map facilitated interpretation. This study was small in scope, but it provides one piece of knowledge about the information needs related to algorithmic classifications.
How to Combine Variational Bayesian Networks in Federated Learning
Venue: arXiv cs.LG 2022
Year: 2022

Federated Learning enables multiple data centers to train a central model collaboratively without exposing any confidential data. Even though deterministic models are capable of performing high prediction accuracy, their lack of calibration and capability to quantify uncertainty is problematic for safety-critical applications. Different from deterministic models, probabilistic models such as Bayesian neural networks are relatively well-calibrated and able to quantify uncertainty alongside their competitive prediction accuracy. Both of the approaches appear in the federated learning framework; however, the aggregation scheme of deterministic models cannot be directly applied to probabilistic models since weights correspond to distributions instead of point estimates. In this work, we study the effects of various aggregation schemes for variational Bayesian neural networks. With empirical results on three image classification datasets, we observe that the degree of spread for an aggregated distribution is a significant factor in the learning process. Hence, we present an investigation on the question of how to combine variational Bayesian networks in federated learning, while providing benchmarks for different aggregation settings.
Improve Sensing and Communication Performance of UAV via Integrated Sensing and Communication
Venue: arXiv cs.NI 2022
Year: 2022

The unmanned aerial vehicle (UAV) needs to sense the environment to ensure safe flight, and the sensing accuracy and communication delay performance are two important indicators of safe flight. The strategy of using integrated sensing and communication (ISAC) technology to improve the sensing and communication performance is proposed in this paper. On the one hand, the extended kalman filter (EKF) algorithm is adopted to achieve the fusion of communication location information and sensing information to improve the accuracy of target sensing. On the other hand, a Identification Friend or Foe (IFF) method based on ISAC is proposed to reduce communication delay. Compared with the traditional IFF method, the integrated technology used for IFF can realize the radar sensing and communication interrogating functions in parallel, greatly shortening the sensing time. Simulation results show that using ISAC technology, the sensing performance of UAV has been greatly improved, the communication delay can be reduced by up to 50%, the accuracy of target sensing can be improved by 24.2% when communication location information and radar sensing information have the same sensing accuracy.
Toward Multiple Integrated Sensing and Communication Base Station Systems: Collaborative Precoding Design with Power Constraint
Venue: arXiv cs.IT 2022
Year: 2022

The collaborative sensing of multiple Integrated sensing and communication (ISAC) base stations is one of the important technologies to achieve intelligent transportation. Interference elimination between ISAC base stations is the prerequisite for realizing collaborative sensing. In this paper, we focus on the mutual interference elimination problem in collaborative sensing of multiple ISAC base stations that can communicate and radar sense simultaneously by transmitting ISAC signals. We establish a mutual interference model of multiple ISAC base stations, which consists of communication and radar sensing related interference. Moreover, we propose a joint optimization algorithm (JOA) to solve the collaborative precoding problem with total power constraint (TPC) and perantenna power constraint (PPC). The optimal precoding design can be obtained by using JOA to set appropriate tradeoff coefficient between sensing and communication performance. The proposed collaborative precoding design algorithm is evaluated by considering sensing and communication performance via numerical results. The complexity of JOA for collaborative precoding under TPC and PPC is also compared and simulated in this paper.
Ordered-Statistics Decoding with Adaptive Gaussian Elimination Reduction for Short Codes
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, we propose an efficient ordered-statistics decoding (OSD) algorithm with an adaptive Gaussian elimination (GE) reduction technique. The proposed decoder utilizes two decoding conditions to adaptively remove GE in OSD. The first condition determines whether GE could be skipped in the OSD process by estimating the decoding error probability. Then, the second condition is utilized to identify the correct decoding result during the decoding process without GE. The proposed decoder can break the ``complexity floor'' in OSD decoders introduced by the GE overhead. Simulation results advise that when compared with the latest schemes in the literature, the proposed approach can significantly reduce the decoding complexity at high SNRs without any degradation in the error-correction capability.